The subject matter disclosed herein relates to medical imaging, and more particularly, to improved iterative reconstruction methodologies in medical imaging.
A computed tomography (CT) imaging system typically includes an imaging beam source (e.g., x-ray source or other suitable source) that projects fan- or cone-shaped imaging beams through an object being imaged, such as a patient, to an array of radiation detectors. The beam is collimated to lie within an X-Y plane, or to cover a set of such planes generally referred to as the “imaging planes.” Intensity of radiation from the beam received at the detector array depends on attenuation of the imaging beam by the object. Attenuation measurements from each detector are acquired separately to produce a transmission profile.
The imaging beam source and the detector array are rotated within a gantry and around the object to be imaged so that a projection angle at which the imaging beam intersects the object constantly changes. A group of imaging beam attenuation measurements (such as integral projection data from the detector array at one gantry angle) is referred to as a “view”. A “scan” of the object comprises a set of views made at varying projection angles, during one or more revolutions of the imaging beam source and detector array.
In an axial scan, the projection data is processed to construct an image that corresponds to one or more two-dimensional slices or other patterns taken through the object. To form these slices or patterns, iterative reconstruction of a full field of view may be performed to increase image quality. Iterative reconstruction refers to a method that forms an image by repeatedly adjusting an existing estimate according to the quality of a match between measured data and simulated measurements from a current estimate of the image. The quality of the image estimate may also be affected by consideration of the characteristics of the image alone, such as its smoothness and/or satisfaction of a pre-established model. Multiple iterations are performed to create a resulting reconstructed image that approximately matches the acquired projection data. A full set of reconstructed images is referred to as a 3-D reconstruction, because the set is formed into a three dimensional representation of the object with each image pixel or picture element corresponding to a single voxel or volume element in the 3-D reconstruction.
Traditionally, direct analytical algorithms, such as the Filtered Back-Projection (FBP) algorithm, have been used to reconstruct images from CT data. Iterative techniques, such as the Maximum A Posteriori Iterative Coordinate Descent (MAP-ICD) algorithm, have also been recently considered for reconstruction of volumetric CT data to provide means to improve general image quality over conventional techniques. It has been demonstrated that reduced noise, enhanced resolution, better low contrast performance, and reduced artifacts, can all be achieved with iterative reconstruction of clinical images.
However, Iterative reconstruction (IR) is not yet available on commercial scanners, which typically use the analytical FBP algorithm or its variants. To enable clinical use, current IR may need to better compete with the spatial resolution properties and artifact level of FBP.